Publication

Publication

Dynamically Structured Holographic Memory (DSHM)
is a cognitive model of associative memory that can be applied
to the problem of recommendation. DSHM uses holographically
reduced representations to encode the associations
between objects that it learns about to generate recommendations.
We compare the recommendations from this
holographic recommender to a user-based collaborative filtering
algorithm on several dataset, including MovieLens,
and two bibliographic datasets from a scientific digital library.
Off-line experiments show that the DSHM recommender
predicts movie ratings as well as collaborative filtering and
much better than collaborative filtering on very sparse bibliographic
data sets. DSHM also has a unified underlying
model that makes multi-dimensional recommendations and
their explanations easier to develop. However, DSHM requires
significant amounts of computational resources to
generate recommendations and it may require a distributed
implementation for it to be practical as a recommender for
large data sets.